Acoustic maps have become a fundamental tool for estimating sound levels on wide areas and for predicting changes in acoustic environment related to changes of use. Acoustic prediction is widely used in the process for planning new industrial areas, wind power stations or for changes in existing structures such as, for example, the introduction of a fan for air-conditioning or road widening. A correct acoustic mapping of the environments helps both to estimate and to check changes in acoustic effect.
Acoustic mapping uses similar modelling techniques for estimating the current acoustic levels on a wide area that could not be determined by an acoustic monitoring system since it is too expensive.
Therefore, the processing of such acoustic maps is a very important aspect, in order to improve and speed up their use.
Therefore, it is considerably important to obtain acoustic map processing that aims at accurately isolating one or more target acoustic sources.
By methods known in prior art it is not possible to obtain a “clean” acoustic map that allows an immediate match and a precise location of the acoustic source of interest to be obtained.
Moreover, one of the most common uses of acoustic mapping is the tracking of a target acoustic source.
As it will be described below, the above mentioned method steps are described by documents about methods for tracking acoustic sources known in the prior art, wherein tracking algorithms use information from acoustic maps and multispectral acoustic images for locating a target acoustic track.
It is specified that, as known in prior art, the multispectral acoustic image consists of a collection of 2D acoustic images, which in turn are formed by the transposition of the position of each individual acquired acoustic source into a grayscale or color model.
Moreover, each 2D acoustic image is identified by a single frequency or a frequency band, such that each 2D acoustic image has the position of each of the detected audio sources marked thereon along the axes of the coordinates of the 2D image, for the spatial allocation of the acquired acoustic sources.
Therefore, it is clear how the multispectral acoustic image is composed of a collection of 2D images each one estimated at a single frequency or frequency bands.
As it will be clear from the description of some embodiments for each frequency, an acoustic map is identified on a 2D plane of the space region where the acquisition has been performed and the values of the pixels of each 2D image denote the space allocation of the acquired acoustic sources.
Moreover, it is specified that the acquisition step can be carried out in any manner known in the prior art.
Preferably, a beamforming algorithm is used in combination with an array of acoustic sensors for acquiring the acoustic sources present in the environment.
A possible example of such method is described in patent application WO2014/115088 to the applicant, whose contents has to be considered as an integral part of the present description.
Moreover, the acquisition step of the method of the present invention can be carried out in any environment: acoustic mapping can be about both an environment in the presence of audio sources where the acquisition is performed through an array of microphones, and underwater environment, where acoustic sources are detected through hydrophones.
Particularly, the invention relates to the processing of acoustic maps of environments with noises and disturbed by acoustic signals different from the target acoustic signal.
According to a preferred embodiment, the method of the present invention aims at solving the problem of locating and tracking an acoustic source, given a set of acoustic signals acquired by an acoustic sensor array.
Such problem is increasingly becoming important since there are many applications that use localization and tracking of an acoustic source.
The tracking of a speaker in a teleconference, the tracking of vehicles in traffic monitoring systems, the localization of general targets and the tracking in surveillance systems and in military applications are only some examples where acoustic source tracking is employed.
In all such applications the use of acoustic systems therefore plays an important role both as a help for video systems and as a real tool replacing the latter.
In particular acoustic systems are particularly suitable for replacing video systems in cases when video information is not available or is not so much useful, such as for example under conditions of poor visibility, in the night, under conditions of adverse weather or in particularly crowded environments.
In the methods known in the prior art it is possible to define different approaches for acoustic localization and tracking that can be divided in different groups:
Time-Difference-of-Arrival (TDOA)=it is about methods where TDOAS are estimated for each pair of acoustic sensors and then used for deducing the location of the acoustic source on the basis of the geometry of the microphone array,
Steered Response Power=it is about methods where the location of the acoustic source is directly estimated by scanning all the possible space locations through a beamforming algorithm and by using the local peaks of the obtained acoustic map.
These two first groups have an important drawback, since typically the acoustic signal is particularly noisy due to reverberations and to noise acoustic sources, therefore the localization of the acoustic source based on a single time interval is subject to the detection of anomalous values due to spurious peaks present in the acoustic map.
In order to face such drawback, the localization and tracking methods known in the prior art use the time consistency of the acoustic source along the several time intervals.
Another method known in the prior art is about the algorithms known as “Batch Approaches [17]” that deduce the trajectory of an acoustic source on the basis of a set of localizations measured in adjacent time intervals.
The main drawback of such algorithms is the need of detecting the whole group of time intervals before estimating the desired trajectory, a characteristic that has a particularly disadvantageous aspect in case of real-time detections.
Other algorithms known in the prior art are those about the “First Order Markov Process approaches” group, where the position of the acoustic source is tried to be found in a specific instant, on the basis of the knowledge of the following instant.
Among such algorithms the Particle Filter [8] is mentioned which is particularly efficient in presence of non-Gaussian disturbing noises provided in positions different than the position of the target acoustic source.
Regardless of the specific tracking algorithms, an alternative approach consists in using the whole acoustic map instead of the set of spread positions where the acoustic source of interest has been identified.
Thus the possible loss in information caused by the insertion of threshold values or by the use of filters is avoided.
Such approach has been followed in the documents [9], [10] and also in document [11] in combination with the concept of Track before Detect (TBD), suggested and disclosed in the document [12].
Another problem to be faced in tracking acoustic sources is the temporary inactivity of the target, such as for example pauses during speech, disclosed in document [13] and in document [11].
The acoustic tracking problem can be about not only one single target source, but also about a plurality of target sources, such as disclosed in documents [11] and [17].
All the methods and approaches described and belonging to the prior art, mainly have an important drawback that creates instability in the algorithms as well as restrictions in using the methods themselves.
In presence of a noise source with high energy and persistent over time, the known prior art tracking algorithms tend to find the noise source instead of the target source.
The acoustic map provides only information about the space allocation of the sound energy and no other information useful for distinguishing the target source from noise sources.
It is obvious how the methods known in prior art are inefficient in environments with noises, that is in most of the environments where generally acoustic localization and tracking are performed.
Also the document LU WENBO ET AL: “A gearbox fault diagnosis scheme based on nearfield acoustic holography and spatial distribution features of sound field”, JOURNAL OF SOUND & VIBRATION, LONDON, GB, vol. 332, no. 10, 4 Feb. 2013 (2013-02-04), pages 2593-2610, XP028985954, ISSN: 0022-460X, DOI: 10.1 016/J. JSV.2012.12.018 describes a method according to the preamble of claim 1.
Therefore there is a need not satisfied by the methods known in prior art to provide a method for processing acoustic images and a consequent method for tracking a target acoustic source that is robust, efficient and that overcomes the drawbacks of the methods known in the prior art, particularly allowing a “clean” acoustic map and not affected by noise acoustic sources to be obtained also in environments with noises and high energy disturbance acoustic signals.